Bayesian inference of spike-timing dependent plasticity learning rules from single neuron recordings in humans

Abstract

Spike-timing dependent plasticity (STDP) learning rules are popular in both neuroscience and artificial neural networks due to their ability to capture the change in neural connections arising from the correlated activity of neurons. Recent technological advances have made large neural recordings common, substantially increasing the probability that two connected neurons are simultaneously observed, which we can use to infer functional connectivity and associated learning rules. We use a Bayesian framework and assume neural spike recordings follow a binary data model to infer the connections and their evolution over time from data using STDP rules. We test the resulting method on simulated and real data, where the real case study consists of human electrophysiological recordings. The simulated case study allows validation of the model, and the real case study shows that we are able to infer learning rules from awake human data.